Metatranscriptomics-based investigation of bacterial community dynamics across a dissolved organic matter gradient in southern Lake Michigan.
The main results reproduced: recomputed values matched the published ones within tolerance.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
- ✓Any deviation was negligible
- ✓The central claim held under reproduction
- 🟡Could not use the authors’ exact input data
- 🟡Reported values were only indirectly comparable
- 🟡A deviation arose in the data or preprocessing
- 🟡A deviation was attributed to the published material
- 🟡Reported values were not (fully) derivable from the shared data
- 🟡Overall, the reproduction showed a material discrepancy
A 0–100 reproducibility-quality score from the per-question grades, shown as a z-score: standard deviations above (+) or below (−) the mean of comparable assessments.
▸Reproduction agent’s raw note
Described well enough for a PARTIAL 1:1. The paper's only listed code is the generic R vegan package, used for a single non-significant PERMANOVA whose input (a 5,316 feature x 11 sample community matrix) was never deposited and for which no numeric statistic is printed -- so the literal vegan result is not reproducible (C5), as is the 5,316-feature count (C6), because re-deriving it needs JGI's proprietary IMG/M annotation of the raw SRA (the hard, non-faithful 80%, deliberately not attempted; no «our HPC» job warranted). Instead I audited the paper's headline numbers against its OWN deposited derived data (supplementary Tables S1/S3/S4 + the deposited MAG fasta repo) and reproduced three EXACTLY: the central claim of 130 differentially expressed gene families nearshore-vs-offshore (Table S4 = exactly 130 MaAsLin2 features, all q<0.05); 11 libraries = 3 nearshore + 8 offshore (Table S1); and 7 MAG populations (Table S3, independently corroborated by 7 matching fasta in github.com/Aditchaudhary/Lake-Michigan-MAGs). One within-tol flag: deposited per-library read counts (43.53-58.17 M) sit just below the paper's stated 43.8-58.6 M range (<1%, likely a counting/rounding convention). No fabrication signal on the checkable claims; the unverifiable ones (C5/C6) simply lack released data. NOT attempted: full assembly/IMG-M annotation/normalization pipeline, vegan PERMANOVA, PCoA (ecodist), taxonomy/DOC/transporter/stress-marker content (wet-lab/external).
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Assessment versions
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v1 current initial assessment Score 81assessed: 2026-06-16 ⛓ aed1e4ca9c34
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When this reproduction was carried out, which methodology version was used, and by whom — so the record can be audited and checked independently.
- Reproduced
- 2026-06-16
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-16no human curator yet
- Last updated
- 2026-08-05
Provisional, curator- or AI-assessed, and independently checkable. A reproduction outcome states what one attempt could reproduce — not a judgement of the authors.
Deep full-text extraction
Model: opusHow do environmental controls—particularly the quality and gradient of dissolved organic matter (DOM)—shape bacterioplankton community function and substrate-acquisition strategies across a nearshore-to-offshore transect in southern Lake Michigan?
- ★ DOM composition changes significantly across the nearshore-to-offshore transect, with more terrestrially derived and high-molecular-weight DOM nearshore, despite only minor reductions in DOC and similar inorganic N and P levels. finding
- ★ Differences in DOM quality across the transect are associated with differential expression of gene families between nearshore and offshore bacterioplankton. finding
- ★ Genes for acquiring DOM, N, and P substrates (peptidases, proteases, and transporters for amino acids, nucleobases, sugars, urea, and inorganic phosphate) are over-represented in offshore bacterioplankton. finding
- ★ Offshore bacterial communities are more substrate-limited (particularly carbon) than nearshore and invest more energy in acquiring DOM substrates. mechanism
- ★ Focused analysis of transporter gene expression for C, N, and P substrates shows higher expression of DOM transporter genes offshore versus nearshore. finding
- Metatranscriptomics can be applied to assess bacterioplankton metabolism in a large freshwater lake in the context of rich environmental DOM characterization data. method
- Coastal-to-offshore spatial gradients in large lakes provide a study system to investigate bacteria-water chemistry relationships with limited confounding abiotic effects. finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| metatranscriptomics (mRNA-based gene expression) | free-living bacterioplankton (0.2-µm filtered, 1.6-µm prefiltered) from surface waters of southern Lake Michigan nearshore-to-offshore transect | none (spatial environmental gradient; nearshore vs offshore, spring vs summer 2017-2018) | transcript abundance of gene families | Illumina NovaSeq S4, paired-end 150 bp; RiboCop rRNA Depletion kit + CORALL Total RNA-Seq Library Prep kit |
| metagenomics (community gDNA sequencing) | bacterioplankton community DNA from summer 2017 Lake Michigan samples | none | genomic sequence content | Illumina NextSeq, paired-end 150 bp |
| dissolved organic carbon (DOC) measurement | 0.2-µm filtered Lake Michigan surface water | none | DOC concentration (µM) | high-temperature combustion method |
| nutrient analysis | 0.2-µm filtered Lake Michigan surface water | none | orthophosphate (PO4 3-/SRP) and nitrate+nitrite (NOx) concentrations | autoanalyzer AQ300, SEAL Analytical |
| CDOM UV-vis absorption spectroscopy | Lake Michigan surface water filtrate (spring 2018 + some summer 2018) | none | absorption coefficient a254, spectral slope S275-295, slope ratio Sr (proxies for CDOM concentration and molecular weight) | — |
| fluorescence excitation-emission matrix (EEM) / FDOM characterization | Lake Michigan surface water filtrate | none | biological index (BIX), humification index (HIX), fluorescence peaks/components | spectrofluorometer |
- ▼ Higher presence of terrestrially derived and high-molecular-weight DOM in nearshore versus offshore
- ▼ Minor reduction in DOC levels from nearshore to offshore
- – Inorganic N and P measurements similar across the transect
- ▲ DOM-, N-, and P-acquisition genes (peptidases, proteases, transporters for amino acids, nucleobases, sugars, urea, inorganic phosphate) over-represented in offshore bacterioplankton
- ▲ Higher expression of DOM transporter genes for C, N, P substrates offshore versus nearshore
- – Filtered feature table retained 5,316 unique gene families after thresholding 5,316 gene families
- – Metatranscriptome libraries yielded 43.8-58.6 million paired-end reads per library 43.8-58.6 million reads
- count 5,316 unique gene families retained (features with ≥10 transcript counts in ≥30% (4/11) of samples)
- count 43.8-58.6 million paired-end reads per library (metatranscriptome sequencing yield)
- count 11 samples total (3 nearshore NRS, 8 offshore OFS1/OFS2/OFS3) (sampling design across transect 2017-2018)
- other DOC range 93-191 µM (e.g., NRS 191±1.2; OFS3 93±1.4) (DOC concentrations nearshore vs offshore, Table 1)
- other NRS ~3.5 km from shore; OFS ~10-50 km from shore (distance of sampling sites from shore)
- pvalue P < 0.05 (Welch's T test for DOC/DOM parameters significantly different between nearshore and offshore)
Statistical methods review
Model: sonnetA neutral, descriptive read of the statistical approach — what was done, and (for shared learning, not as criticism) what could also have been done.
This observational metatranscriptomic study compared bacterioplankton gene expression between nearshore (n=3) and offshore (n=8) sites along a southern Lake Michigan transect in 2017–2018 across two seasons. Water chemistry parameters (DOC, BIX, HIX, CDOM indices) were compared between locations using Welch's t-tests. Gene family transcript abundances were size-factor-normalized via DESeq2, ordinated with Bray-Curtis dissimilarity/PCoA, and tested for differential expression using MaAsLin2 mixed-effects models with location and season as fixed effects and sampling station and year as random effects (text truncated before full model specification was provided).
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Welch's t-test (two-sample, unequal variance) | Comparison of DOC concentration, BIX, and HIX between nearshore and offshore (Fig. 1B–D) | 3 nearshore vs. up to 8 offshore; CDOM/FDOM subset restricted to spring 2018 and partial summer 2018 samples — exact n per test not stated | not stated |
| Bray-Curtis dissimilarity + principal coordinate analysis (PCoA) | Ordination of DESeq2-normalized gene family profiles across all 11 metatranscriptome samples | 11 samples total | na |
| MaAsLin2 linear mixed-effects model | Detection of gene families differentially associated with location (nearshore/offshore) and season (spring/summer); sampling station and year included as random effects (model specification truncated in provided text) | 11 samples; feature table filtered to genes with ≥10 counts in ≥4 of 11 samples (5,316 gene families retained) | not stated |
| DESeq2 size-factor normalization | Library-size normalization of gene family transcript count matrix prior to all downstream analyses | 11 metatranscriptome libraries | na |
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Welch's t-tests were used to compare individual DOM/water chemistry parameters between nearshore and offshore with P < 0.05 as the threshold, with multiple parameters tested↳ Could also: Apply a Benjamini-Hochberg FDR correction or Bonferroni correction across the family of water chemistry t-tests, or use a single MANOVA to test all DOM indices jointly — When multiple parameters are tested simultaneously, a family-wise or FDR correction reduces the probability of at least one false positive across the test family; MANOVA additionally accounts for correlations among the DOM indices
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With 3 nearshore and up to 8 offshore samples, Welch's t-tests assume approximate normality in small groups↳ Could also: Use a Mann-Whitney U (Wilcoxon rank-sum) test as a non-parametric alternative — Non-parametric tests make no distributional assumption and are often preferred when group sizes are small (n=3 in one group), where normality is difficult to assess
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Bray-Curtis PCoA was used to visualize community-level gene expression structure↳ Could also: Use non-metric multidimensional scaling (NMDS) on the same Bray-Curtis matrix, optionally with a PERMANOVA (adonis2 in vegan) to formally test location/season effects on community composition — NMDS relaxes the linearity assumption of PCoA and often provides better stress-minimized low-dimensional representation; PERMANOVA provides an omnibus statistical test of group separation in multivariate space to complement the ordination plot
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DESeq2 size-factor normalization was applied to the gene family count matrix before MaAsLin2 modeling↳ Could also: Use trimmed mean of M-values (TMM) normalization (edgeR) or centered log-ratio (CLR) transformation, then apply a linear mixed model or permutation-based approach — TMM and CLR are widely used normalization strategies for compositional count data; CLR in particular is compositionally appropriate and is sometimes preferred when downstream analyses assume log-linearity
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DOC and other continuous measurements were reported as mean ± SD↳ Could also: Report 95% confidence intervals alongside or instead of SD — CIs directly communicate uncertainty about the group mean and facilitate comparison across studies; with small n (e.g., n=3 at NRS), CIs are often more informative than SD for conveying precision of the estimate
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Significance for water chemistry differences was reported as a binary P < 0.05 threshold↳ Could also: Report exact p-values and an effect size metric (e.g., Cohen's d or rank-biserial correlation) for each comparison — Exact p-values allow readers to apply alternative thresholds; effect sizes convey the magnitude of the difference independently of sample size, which is particularly informative when n is small
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Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
The paper's three checkable headline numbers — 130 differentially expressed gene families (C1, exact from Table S4, max q=0.0496), 11 libraries = 3 nearshore + 8 offshore (C2), and 7 MAG populations (C3, independently corroborated by 7 matching GitHub fasta) — reproduce 1:1 from the authors' own deposited derived data, with no fabrication signal. The only factual deviation is C4: deposited read-pair counts (43.53–58.17 M) sit <1% below the stated 43.8–58.6 M range, a likely counting/rounding convention. The two non-reproducible claims (C5 PERMANOVA, C6 5,316 features) fail on data availability — the count/community matrices were never deposited and the JGI IMG/M annotation step is proprietary — not on any authors' defect or contradiction. Net: a solid partial reproduction; the central conclusion holds while the heavy upstream community-matrix results remain unverifiable.
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